Cloud computing is a new era in computing paradigm. It helps Information Technology (IT) companies to cut the cost by outsourcing data and computation on-demand. Cloud computing provides different kind of services which includes Hardware as a Service, Software as a Service (SaaS), Infrastructure as a Service (IaaS) etc. Despite these potential benefits, many IT companies are reluctant to do cloud business due to outstanding trust issues. Cloud consumer and provider are the most interested parties to maximize their benefits. In IaaS, the cloud provider operates the whole computing platform as a resource for the customer, which is accessed by customer as a Virtual Machine (VM) via the internet. The cloud provider must predict the best machine among the available machines to launch VM. This strategic prediction would avoid exodus of computation in middle due to machine heavy load or any failure which severely affect the benefits of both consumer and provider. Since VM allocation for IaaS request is a challenging task, in this study novel architecture is proposed for IaaS cloud computing environment in which VM allocation is done through genetically weight optimized neural network. In this scenario the host load of each machine is taken as its resource information. The neural network predicts the host load of each machine in near future based on the recent past host load. It would help the VM allocator to choose the right machine. Analysis is done on the performance of genetically weight optimized Back Propagation Neural Network (BPNN), Elman Neural Network (ELNN) and Jordan Neural Network (JNN) for prediction accuracy.